Lens-free holographic imaging may provide a low-cost solution for imaging small objects, as it typically does not require expensive or complex optical components. Lens-free holographic imaging may also provide a relatively large field of view when compared with compact conventional microscopes using lenses. Furthermore, holographic imaging allows good depth of field imaging, such that a large volume can be imaged by a single image acquisition.
However, in many cases, such as in automatic inspection of objects, the distance between the object of interest and the image detector is not known in advance, e.g. this distance can be variable and may have a significant stochastic component. Conventional digital holographic reconstruction algorithms, e.g. using forward and backward propagation of the optical fields, may typically require such focal distance to be provided as a parameter to obtain a high quality reconstruction. Since an incorrect focus may result in blurred images and may cause difficulties in specific applications, such as cell behavior analysis, it may be desirable to use a method to find optimized focal planes automatically, and to provide a corresponding autofocus system.
Conventional techniques use scalar images comprising gradient magnitude values as function of image coordinates to determine a suitable focal plane for an object of interest in lens-free holographic imaging. Such approaches may be based on spatial gradient analysis of holographic reconstruction images consisting of reconstructed wave amplitude values or relates quantities, e.g. scalar image intensities. Such methods may thus be characterized as amplitude-based approaches.
For example, in a paper entitled “Detection of Waterborne Parasites Using Field-Portable and Cost-Effective Lensfree Microscopy” by Onur Mudanyali et al., an amplitude image is determined based on an image gradient magnitude as function of two-dimensional image coordinates, wherein the image gradient is approximated by horizontal and vertical Sobel operator convolutions of a reconstructed image. The variance of this amplitude image is used as a focus measure, where this focus measure reaches a maximum at a reconstruction focal distance where good sharpness and contrast are obtained.
In a paper entitled “Fast Autofocus Algorithm for Automated Microscopes” by Mario A. Bueno-Ibarra et al., a focus measure is determined for images obtained by conventional microscopic imaging at different focal distances. The present disclosure describes a focus measure based on the variance of the magnitude of the Sobel-Tenengrad gradient (SOB VAR). The disclosure also describes a focus measure based on the variance of the absolute value of the convolution of the image with a discrete Laplace operator (LAP VAR).
While such amplitude-based approaches are widely used, scalar focus measures determined from scalar images obtained by manipulation of image derivatives may include a global search in the entire depth range of interest is conducted to determine an optimized focal plane by maximization of the scalar focus measure. Therefore, narrowing the search range down may allow speed to be improved.